历史渔获量统计偏差对资源评估的影响：以西大西洋蓝鳍金枪鱼为例

冯佶; 朱江峰; 张帆; 李亚楠; 耿喆

doi:10.12131/20220037

历史渔获量统计偏差对资源评估的影响：以西大西洋蓝鳍金枪鱼为例

doi: 10.12131/20220037

冯佶^1,,
朱江峰^{1, 2, 3, ,},
张帆¹,
李亚楠¹,
耿喆¹

1.
上海海洋大学海洋科学学院，上海 201306
2.
大洋渔业资源可持续开发教育部重点实验室，上海 201306
3.
农业农村部大洋渔业开发重点实验室，上海 201306

基金项目: 国家自然科学基金项目 (41676120, 32002393)

详细信息

作者简介:
冯佶：冯　佶 (1993—)，男，博士研究生，研究方向为渔业资源评估。E-mail: 276828719@qq.com

通讯作者:
朱江峰 (1978—)，男，教授，博士，研究方向为渔业资源评估与种群生态学。E-mail: jfzhu@shou.edu.cn

中图分类号: S 932
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- 被引次数: 0
出版历程
- 收稿日期: 2022-02-21
- 修回日期: 2022-04-18
- 录用日期: 2022-04-18
- 网络出版日期: 2022-10-21
- 刊出日期: 2023-02-03

Influence of statistical deviation of historical catch on stock assessment: a case study of western Atlantic Thunnus thynnus

FENG Ji^1
,,
ZHU Jiangfeng^{1, 2, 3
, ,},
ZHANG Fan¹,
LI Yanan¹,
GENG Zhe¹

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Shanghai Ocean University/Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

摘要

摘要: 渔获量数据是资源评估所需的最基本数据，同时也最易出现报告和统计误差。误报问题是导致历史渔获量偏差的原因之一，普遍存在于全球各类渔业资源评估中。根据历史数据，分析渔获量偏差对资源评估的影响有助于建立合理的管理目标，促进渔业资源可持续利用。以西大西洋蓝鳍金枪鱼 (Thunnus thynnus) 为例，运用年龄结构模型 (Age-Structured Assessment Program, ASAP)，分析历史渔获量统计偏差对当前资源状态判定的影响。结果表明，捕捞死亡系数 (Fishing mortality, F) 和产卵亲体生物量 (Spawning stock biomass, SSB) 的估计值会随着调整后的实际渔获量同向变化；随着统计偏差幅度增大，F和SSB相关生物学参考点的相对偏差率也随之增大。所有8种假定的渔获量统计偏差情况下，F相关参考点的相对偏差率均小于1%；当渔获量统计偏差为−20%时，SSB相关参考点的最大相对偏差率约为4%。历史渔获量统计偏差对SSB相关参考点的影响相比F相关参考点更为明显。根据该研究结果，建议加强渔获量数据质量问题的来源分析，从而进行历史渔业数据的科学重建，以提高评估结果的精确性与可信度。
- 蓝鳍金枪鱼 /
- 资源评估 /
- 渔获量 /
- 年龄结构模型 /
- 大西洋
Abstract: Catch data, which is the most basic data for stock assessment, is also most likely to cause reporting and statistical errors. Misreporting is one of the causes for statistical deviation of historical catch, which is currently prevalent in all types of fisheries worldwide. Analyzing the influence of statistical deviation of historical catch on stock assessment based on historical data helps to establish reasonable management objectives, and promote sustainable utilization of fishery resources. In this study, we selected western Atlantic bluefin tuna (Thunnus thynnus) as an example to evaluate the influence of statistical deviation of historical catch on its stock assessment. We carried out a stock assessment by using Age-Structured Assessment Program (Age-Structured Assessment Program, ASAP), and investigated the effects of catch information inaccuracy on the assessment results by setting different levels of statistical deviation of historical catch. The results indicate that the estimated values of fishing mortality (F) and spawning stock biomass (SSB) changed in the same direction with the adjusted catch. With the increase of statistical deviation of catch, the relative difference of biological reference points also increased. The relative deviation rate of F-related biological reference points was less than 1% under all eight assumed statistical deviations of catch. When the statistical deviation of the historical catch was assumed as −20%, the maximum relative difference of SSB-related biological reference points was about 4%. The statistical deviation of catch had a more obvious impact on SSB-related biological reference points than F-related biological reference points. In conclusion, it is suggested to strengthen the source analysis of catch data quality issues, so that the scientific reconstruction of historical fishery data can be conducted to improve the accuracy and reliability of the stock assessment results.
- Thunnus thynnus /
- Stock assessment /
- Catch /
- Age-Structured Assessment Program /
- Western Atlantic Ocean

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图 1 评估模型假设的西大西洋蓝鳍金枪鱼自然死亡系数与成熟度

Figure 1. Natural mortality and maturity at age as assumed in assessment models of T. thynnus

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图 2 基准模型年渔获量观测值与预测值

Figure 2. Observed and predicted values of annual catch for base case model

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图 3 基准模型丰度指数观测值与预测值

Figure 3. Observed and predicted values of abundance index for base case model

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图 4 测试 1—测试 3的年渔获量残差变化

Figure 4. Residual of annual total catch of Test 1 to Test 3

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图 5 测试 1—测试 3的丰度指数US_RR_66_114残差变化

Figure 5. Residual of abundance index US_RR_66_114 of Test 1 to Test 3

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图 6 基准模型有效样本量输入值和估算值

Figure 6. Input and estimated effective sample size for base case model

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图 7 不同渔获量统计偏差的捕捞死亡系数

Figure 7. Fishing mortality with different statistical deviation of catch for T. thynnus

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图 8 不同渔获量统计偏差的产卵亲体生物量

Figure 8. Spawning stock biomass with different statistical deviation of catch for T. thynnus

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图 9 不同测试的生物学参考点的相对偏差率

Figure 9. Relative difference of biological reference points with different test results

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表 1 西大西洋蓝鳍金枪鱼评估模型使用的丰度指数

Table 1. Abundance index used in assessment models for T. thynnus

丰度指数名称 Name of abundance index	时间跨度 Time period/年	描述 Description
US_RR_66_114	1993—2015	美国竿钓指数资料 (66~114 cm 体长组)
US_RR_115_144	1993—2015	美国竿钓指数资料 (115~144 cm 体长组)
US_RR<145	1980—1983、1985—1992	美国竿钓指数资料 (<145 cm 体长组)
US_RR>195	1983—1992	美国竿钓指数资料 (>195 cm 体长组)
US_RR>177	1993—2015	美国竿钓指数资料 (>177 cm 体长组)
JLL_AREA_2	1976—2009	日本延绳钓指数资料
JLL_RECENT	2010—2015	日本延绳钓指数资料
GOM_PLL	1992—2015	墨西哥湾 (Gulf of Mexico, GOM) 延绳钓指数资料
CAN_Combined_RR	1984—2015	加拿大竿钓综合指数资料
CAN_GSL_Acoustic	1994—2015	加拿大声学调查
LARVAL	1977—1978、1981—1984、1986—2015	幼鱼调查

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表 2 测试场景假设及敏感性分析

Table 2. Test scenarios and sensitivity analysis models for T. thynnus

测试 Test	渔获量统计偏差 Statistical deviation of catch
测试 1 Test 1	无偏差
测试 2 Test 2	−20% (1950—1969 年)、0% (1970—2015 年)
测试 3 Test 3	−15% (1950—1969 年)、0% (1970—2015 年)
测试 4 Test 4	−10% (1950—1969 年)、0% (1970—2015 年)
测试 5 Test 5	−5% (1950—1969 年)、0% (1970—2015 年)
测试 6 Test 6	5% (1950—1969 年)、0% (1970—2015 年)
测试 7 Test 7	10% (1950—1969 年)、0% (1970—2015 年)
测试 8 Test 8	15% (1950—1969 年)、0% (1970—2015 年)
测试 9 Test 9	20% (1950—1969 年)、0% (1970—2015 年)

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表 3 各测试的评估结果及其相对偏差率

Table 3. Stock assessment results and relative differences for each test

测试 Test	渔获量统计偏差 Statistical deviation of catch	目标函数 Objective function	生物学参考点及相对偏差率 Biological reference points and relative differences
测试 Test	渔获量统计偏差 Statistical deviation of catch	目标函数 Objective function	MSY/t	RD/%	F_MSY	RD/%	F_cur/F_MSY	RD/%
测试 1 Test 1	无偏差	2 576.88	4 861.48	0.00	0.045 861	0.000	0.751 3	0.00
测试 2 Test 2	−20%	2 565.39	5 025.08	3.37	0.045 872	0.024	0.756 1	0.64
测试 3 Test 3	−15%	2 568.40	4 974.02	2.31	0.045 872	0.023	0.754 7	0.45
测试 4 Test 4	−10%	2 571.32	4 930.35	1.42	0.045 867	0.015	0.753 5	0.29
测试 5 Test 5	−5%	2 574.14	4 893.21	0.65	0.045 862	0.002	0.752 4	0.15
测试 6 Test 6	5%	2 579.55	4 828.90	−0.67	0.045 859	−0.002	0.750 2	−0.15
测试 7 Test 7	10%	2 582.14	4 800.81	−1.25	0.045 853	−0.017	0.749 3	−0.27
测试 8 Test 8	15%	2 585.16	4 772.35	−1.83	0.045 852	−0.018	0.748 1	−0.43
测试 9 Test 9	20%	2 587.12	4 756.96	−2.15	0.045 848	−0.028	0.747 4	−0.52

测试 Test	渔获量统计偏差 Statistical deviation of catch	目标函数 Objective function	生物学参考点及相对偏差率 Biological reference points and relative differences
测试 Test	渔获量统计偏差 Statistical deviation of catch	目标函数 Objective function	SSB_MSY/t	RD/%	SSB_cur/SSB_MSY	RD/%	SSB_cur/SSB₀	RD/%
测试 1 Test 1	无偏差	2 576.88	99 681.1	0.00	0.510 3	0.00	0.193 1	0.000
测试 2 Test 2	−20%	2 565.39	102 951.0	3.28	0.490 5	−3.88	0.185 6	−0.039
测试 3 Test 3	−15%	2 568.40	101 920.0	2.25	0.496 5	−2.70	0.187 9	−0.027
测试 4 Test 4	−10%	2 571.32	101 051.0	1.37	0.501 7	−1.69	0.189 9	−0.017
测试 5 Test 5	−5%	2 574.14	100 316.0	0.64	0.506 3	−0.78	0.191 6	−0.008
测试 6 Test 6	5%	2 579.55	99 028.4	−0.65	0.514 5	0.82	0.194 7	0.008
测试 7 Test 7	10%	2 582.14	98 479.1	−1.21	0.518 2	1.55	0.196 1	0.016
测试 8 Test 8	15%	2 585.16	97 910.1	−1.78	0.522 1	2.31	0.197 6	0.023
测试 9 Test 9	20%	2 587.12	97 611.8	−2.08	0.524 3	2.74	0.198 4	0.027

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